A computer-implemented method, a computer program product, and a computer system for placements of workloads in a system of multiple platforms as a service. A computer detects a triggering event for modifying a matrix that pairs respective workloads on respective platforms and includes attributes of running respective workloads on respective platforms. The computer recalculates the attributes in the matrix, in response to the triggering event being detected. The computer determines optimal placements of the respective workloads on the respective platforms, based on information in the matrix. The computer places the respective workloads on the respective platforms, based on the optimal placements.

At least one processor is configured to obtain measurement information comprising an indication of an amount of utilization of a hardware resource of a first server node by a plurality of processing groups and to determine that the amount of utilization of the hardware resource is above a threshold amount of utilization. The at least one processor is further configured to select a given processing group for redistribution based at least in part on the determination that the amount of utilization of the hardware resource is above the threshold amount and on an amount of utilization of the hardware resource by the given processing group. The at least one processor is further configured to determine that a second server node comprises enough available capacity of the hardware resource and to redistribute the given processing group to the second server node based at least in part on the determination.

An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

Example methods are provided to deploy an Internet of Things (IoT) solution in a hybrid environment. The methods include deploying a first agent application on a first edge gateway of a first vendor by the first edge gateway. The first agent application is configured to collect a first set of information associated with the first edge gateway. The methods include deploying a second agent application on a second edge gateway of a second vendor by the second edge gateway. The second agent application is configured to collect a second set of information associated with the second edge gateway. In response to a determination of a first virtualized computing environment on the first edge gateway or a second virtualized computing environment on the second edge gateway fulfils a first requirement of a template to deploy the IoT solution, the methods include deploying the IoT solution in the first virtualized computing environment, the second virtualized computing environment, or both.

One example method includes evaluating code of a quantum circuit, estimating one or more runtime statistics concerning the code, generating a recommendation based on the one or more runtime statistics, and the recommendation identifies one or more resources recommended to be used to execute the quantum circuit, checking availability of the resources for executing the quantum circuit, allocating resources, when available, sufficient to execute the quantum circuit, and using the allocated resources to execute the quantum circuit.

A computer-implemented method, a computer program product, and a computer system for optimizing workload placements in a system of multiple platforms as a service. A computer first places respective workloads on respective platforms that yield lowest costs for the respective workloads. The computer determines whether mandatory constraints are satisfied. The computer checks best effort constraints, in response to the mandatory constraints being satisfied. The computer determines a set of workloads for which the best effort constraints are not satisfied and determines a set of candidate platforms that yield the lowest costs and enable the best effort constraints to be satisfied. From the set of workloads, the computer selects a workload that has a lowest upgraded cost and updates the workload by setting an upgraded platform index.

Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.

Techniques for executing machine learning (ML) models including receiving an indication to execute an ML model on a processing core; determining a resource allocation for executing the ML model on the processing core; determining that a layer of the ML model will use a first amount of the resource, wherein the first amount is more than an amount of the resource allocated; determining that an adaptation may be applied to executing the layer of the ML model; executing the layer of the ML model using the adaptation, wherein executing the layer using the adaptation reduces the first amount of the resource used by the layer as compared to executing the layer without using the adaptation; and outputting a result of the ML model based on the executed layer.

Scalable I/O Virtualization (Scalable IOV) allows efficient and scalable sharing of Input/Output (I/O) devices across a large number of containers or Virtual Machines. Scalable IOV defines the granularity of sharing of a device as an Assignable Device Interface (ADI). In response to a request for a virtual device composition, an ADI is selected based on affinity to the same NUMA node as the running virtual machine, utilization metrics for the Input-Output Memory Management Unit (IOMMU) unit and utilization metrics of a device of a same device class. Selecting the ADI based on locality and utilization metrics reduces latency and increases throughput for a virtual machine running critical or real-time workloads.

Various systems and methods for implementing observe-notify callback context automation in a connected device framework are described herein. In an example, the techniques for context automation may include: expansion of RESTful permissions to include an OBSERVE command (e.g., as part of a CRUDON (Create, Retrieve, Update, Delete, Observe, Notify) command definition); configuration of a callback resource to implement the OBSERVE command; access control policies to implement the OBSERVE command; and OBSERVE registration events to be monitored within an access management service.